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相关概念视频

Introduction to Test of Independence01:21

Introduction to Test of Independence

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
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McNemar's Test01:23

McNemar's Test

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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
302
Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.6K
Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

Updated: Jul 20, 2025

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
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对于依赖单个情况下的观测AB相设计数据的变换距离测试:蒙特卡洛模拟研究.

Anouk Vroegindeweij1, Linde N Nijhof2, Patrick Onghena3

  • 1Department of Pediatric Rheumatology/Immunology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.

Behavior research methods
|August 1, 2023
PubMed
概括

变距离测试 (PDT) 有效地评估单个病例观测数据中的治疗效果,显示出高功率和可靠的错误率,特别是在更多的观测和更高的自身相关性的情况下.

关键词:
自动相关性 自动相关性蒙特卡洛模拟的蒙特卡洛模拟换算方式 换算方式 换算方式变换间隔测试试验 变换间隔测试试验一个案例的观察设计.

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科学领域:

  • 统计 统计 统计 统计
  • 行为科学 行为科学
  • 单个案例研究设计

背景情况:

  • 在具有依赖性的单个病例观测设计 (SCOD) 数据中评估治疗效应具有挑战性.
  • 现有的方法,如单个案例随机化测试 (SCRT) 和传统的排列测试,在功率和错误控制方面存在局限性,特别是自相关性.

研究的目的:

  • 引入和评估用于分析依赖 SCOD AB 阶段数据的变换距离测试 (PDT).
  • 将PDT的统计功率和I型错误率与SCRT和传统的排列试验进行比较.

主要方法:

  • 使用蒙特卡洛模拟来估计PDT功率和I型错误率.
  • 通过各种治疗效果水平,自身相关性水平和观察数 (30,60,90,120) 来模拟数据.
  • 与单个案例随机化试验 (SCRT) 和传统的排列试验进行了比较.

主要成果:

  • PDT证明了足够的功率 (≥80%) 来检测中等治疗效应,在30个观察结果中,自相关性 ≤ .45.
  • 在60次观测时,PDT无论自相关性如何,都达到足够的功率;在≥90次观测时,它检测到小效应,自相关性≤0.30.
  • PDT保持了可接受的I型错误率 (≤5%的≥60个观测和自相对应率<.60),在功率和错误控制方面超过了SCRT和传统的排列测试.

结论:

  • 变距离测试 (PDT) 是一种强大的非参数方法,用于分析没有线性趋势的依赖SCOD AB相数据.
  • 与现有方法相比,PDT提供了更好的功率和I型错误控制,特别是在存在自相关性和较少观察的情况下.